• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1135-1147
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• 2021

In order to geometrically correct high-resolution satellite imagery, the sensor modeling process that restores the geometric relationship between the satellite sensor and the ground surface at the image acquisition time is required. In general, high-resolution satellites provide RPC (Rational Polynomial Coefficient) information, but the vendor-provided RPC includes geometric distortion caused by the position and orientation of the satellite sensor. GCP (Ground Control Point) is generally used to correct the RPC errors. The representative method of acquiring GCP is field survey to obtain accurate ground coordinates. However, it is difficult to find the GCP in the satellite image due to the quality of the image, land cover change, relief displacement, etc. By using image maps acquired from various sensors as reference data, it is possible to automate the collection of GCP through the image matching algorithm. In this study, the RPC of KOMPSAT-3A satellite image was corrected through the extracted matching point using the UAV (Unmanned Aerial Vehichle) imagery. We propose a pre-porocessing method for the extraction of matching points between the UAV imagery and KOMPSAT-3A satellite image. To this end, the characteristics of matching points extracted by independently applying the SURF (Speeded-Up Robust Features) and the phase correlation, which are representative feature-based matching method and area-based matching method, respectively, were compared. The RPC adjustment parameters were calculated using the matching points extracted through each algorithm. In order to verify the performance and usability of the proposed method, it was compared with the GCP-based RPC correction result. The GCP-based method showed an improvement of correction accuracy by 2.14 pixels for the sample and 5.43 pixelsfor the line compared to the vendor-provided RPC. In the proposed method using SURF and phase correlation methods, the accuracy of sample was improved by 0.83 pixels and 1.49 pixels, and that of line wasimproved by 4.81 pixels and 5.19 pixels, respectively, compared to the vendor-provided RPC. Through the experimental results, the proposed method using the UAV imagery presented the possibility as an alternative to the GCP-based method for the RPC correction.

• Journal of Bio-Environment Control
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• v.30 no.4
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• pp.278-286
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• 2021

In this study, based on the Computational Fluid Dynamics (CFD) simulation model developed through previous study, inner environmenct of the modified glass greenhouse was predicted. Also, suggested the optimal shape of the greenhouse and location of the heat exchangers for heat energy management of the greenhouse using the developed model. For efficient heating energy management, the glass greenhouse was modified by changing the cross-section design and the location of the heat exchanger. The optimal cross-section design was selected based on the cross-section design standard of Republic of Korea's glass greenhouse, and the Fan Coil Unit(FCU) and the radiating pipe were re-positioned based on "Standard of greenhouse environment design" to enhance energy saving efficiency. The simulation analysis was performed to predict the inner temperature distribution and heat transfer with the modified greenhouse structure using the developed inner environment prediction model. As a result of simulation, the mean temperature and uniformity of the modified greenhouse were 0.65℃, 0.75%p higher than those of the control greenhouse, respectively. Also, the maximum deviation decreased by an average of 0.25℃. And the mean age of air was 18 sec. lower than that of the control greenhouse. It was confirmed that efficient heating energy management was possible in the modified greenhouse, when considered the temperature uniformity and the ventilation performance.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.275-290
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• 2021

Sulfur dioxide (SO2) in the atmosphere is mainly generated from anthropogenic emission sources. It forms ultra-fine particulate matter through chemical reaction and has harmful effect on both the environment and human health. In particular, ground-level SO2 concentrations are closely related to human activities. Satellite observations such as TROPOMI (TROPOspheric Monitoring Instrument)-derived column density data can provide spatially continuous monitoring of ground-level SO2 concentrations. This study aims to propose a 2-step residual corrected model to estimate ground-level SO2 concentrations through the synergistic use of satellite data and numerical model output. Random forest machine learning was adopted in the 2-step residual corrected model. The proposed model was evaluated through three cross-validations (i.e., random, spatial and temporal). The results showed that the model produced slopes of 1.14-1.25, R values of 0.55-0.65, and relative root-mean-square-error of 58-63%, which were improved by 10% for slopes and 3% for R and rRMSE when compared to the model without residual correction. The model performance by country was slightly reduced in Japan, often resulting in overestimation, where the sample size was small, and the concentration level was relatively low. The spatial and temporal distributions of SO2 produced by the model agreed with those of the in-situ measurements, especially over Yangtze River Delta in China and Seoul Metropolitan Area in South Korea, which are highly dependent on the characteristics of anthropogenic emission sources. The model proposed in this study can be used for long-term monitoring of ground-level SO2 concentrations on both the spatial and temporal domains.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.29-37
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• 2019

Textile quality management targets the physical properties of fabrics and the subjective discriminations of color and fitting. Color is the most representative quality factor that consumers can use to evaluate quality levels without any instruments. For this reason, quantification using a color discrimination device has been used for statistical quality management in the textile industry. However, small and medium-sized domestic textile manufacturers use only visual inspection for color discrimination. As a result, color discrimination is different based on the inspectors' individual tendencies and work procedures. In this research, we want to develop a textile industry-friendly quality management method, evaluating the possibility of rapid color discrimination using a digital imaging device, which is one of the office-automation instruments. The results show that an imaging process-based color discrimination method is highly correlated with conventional color discrimination instruments ($R^2=0.969$), and is also applicable to field discrimination of the manufacturing process, or for different lots. Moreover, it is possible to recognize quality management factors by analyzing color components, ${\Delta}L$, ${\Delta}a$, ${\Delta}b$. We hope that our rapid discrimination method will be a substitute technique for conventional color discrimination instruments via elaboration and optimization.

• Journal of the Korean Electrochemical Society
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• v.22 no.1
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• pp.36-42
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• 2019

A macroporous Sn thick film as a high capacity negative electrode for a lithium ion secondary battery was prepared by using a chemical etching method using nitric acid for a Sn film having a thickness of $52{\mu}m$. The porous Sn thick film greatly reduced the over-voltage for the alloying reaction with lithium by the increased reaction area. At the same time. The porous structure of active Sn film plays a part in the buffer and reduces the damage by the volume change during cycles. Since the porous Sn thick film electrode does not require the use of the binder and the conductive carbon black, it has substantially larger energy density. As the concentration of nitric acid in etching solution increased, the degree of the etching increased. The etching of the Sn film effectively proceeded with nitric acid of 3 M concentration or more. The porous Sn film could not be recovered because the most of Sn was eluted within 60 seconds by the rapid etching rate in the 5 M nitric acid. In the case of etching with 4 M nitric acid for 60 seconds, the appropriate porous Sn film was formed with 48.9% of weight loss and 40.3% of thickness change during chemical acid etching process. As the degree of etching of Sn film increased, the electrochemical activity and the reversible capacity for the lithium storage of the Sn film electrode were increased. The highest reversible specific capacity of 650 mAh/g was achieved at the etching condition with 4 M nitric acid. The porous Sn film electrode showed better cycle performance than the conventional electrode using a Sn powder.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.1
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• pp.201-210
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• 2019

Recently, due to the efforts to mitigate traffic congestion and expansion of space efficiency, the construction of underground roads has been increased in big-scale cities. Since tunnels in the city have a higher chance for a fire leading to a great tragedy during a severe traffic jam than mountain tunnels, it is highly likely that it will be constructed as a tunnel, having a small cross section, for small vehicles. However, if they are constructed as such small-vehicle tunnels, it would be possible to reduce the design fire intensity while the concentration of harmful gases would increase due to a reduction in the small cross sectional area, led by a decrease in the tunnel height. In this study, behaviors of fire smoke by the installation interval and format of large-scale exhaust-gas ports were examined and compared in the analysis of temperatures and CO concentrations of a tunnel and its results were as the following. Although there were no significant differences in the smoke spreading distance between installation intervals, but in this study, 100 m was found to be the most effective installation interval. The smoke exhaustion performance was found to be excellent in the order of $4m{\times}3m$, $6m{\times}2m$, and $3m{\times}2m$ (2 lane) of the smoke spreading distance. Although there was no significant difference in the smoke spreading distance between formats of large-scale exhaust-gas ports, it was found that the smoke spreading distance was larger than other cases when it was $3m{\times}2m$ in the fire growing process. The analysis of smoke spreading distances by the aspect ratio showed that a smoke spreading distance was shorted when its the smoke spreading distance was found to be shorter when its traverse distance was relatively longer than its longitudinal distance.

• Korean Chemical Engineering Research
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• v.59 no.4
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• pp.574-583
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• 2021

Waste air containing ethanol (100 ppmv) and hydrogen sulfide (10 ppmv) was continuously treated by waste air-treating system composed of two annular photocatalytic reactors (effective volume: 1.5 L) packed with porous SiO₂ media carrying TiO₂-anatase photocatalyst, one of which was alternately operated for 32 d/run while the other was regenerated by 100 ℃ hot air with 15 W UV(-A)-light on. As its elimination-behavior of ethanol, the removal efficiencies of ethanol at 1^st, 2^nd and 3^rd operation of the photocatalytic reactor system(A), turned out to be ca. 60, 55 and 54%, respectively, at their steady state condition. Unlike the elimination-behavior of ethanol, its hydrogen sulfide-elimination behavior showed repeated decrease of hydrogen sulfide removal efficiency by its resultant arrival at a lower level of steady state condition. Nevertheless, the removal efficiencies of hydrogen sulfide at 1^st, 2^nd and 3^rd operation of the photocatalytic reactor system, turned out to be ca. 80, 75 and 73%, respectively, at their final steady state condition, higher by ca. 20, 20 and 19% than those of ethanol, respectively. Therefore, assuming that adsorption on porous SiO₂-photocatalyst carrier was regarded to belong to a reversible deactivation and that decreased % of removal efficiency due to the reversible deactivation of photocatalyst including the adsorption was independent of the number of its use upon regeneration, the increments of the decreased % of removal efficiency of ethanol and hydrogen sulfide, due to an irreversible deactivation of photocatalyst, for the 3^rd use of regenerated photocatalyst, compared with the 2^nd use of regenerated photocatalyst, were ca. 1 and 2%, respectively, which was insignificant or the less than those of ca. 5 and 5%, respectively, for the 2^nd use of regenerated photocatalyst compared with the 1^st use of virgin photocatalyst. This trend of the photocatalytic reactor system was observed to be similar to that of the other alternately-operating photocatalytic reactor system.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Microbiology and Biotechnology Letters
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• v.49 no.1
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• pp.111-119
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• 2021

A bacterial strain isolated from a Malva verticillata leaf was identified as Bacillus velezensis MV2 based on the 16S rRNA sequencing results. Complete genome sequencing revealed that B. velezensis MV2 possessed a single 4,191,702-bp contig with 45.57% GC content. Generally, Bacillus spp. are known to produce diverse antimicrobial compounds including bacteriocins, polyketides, and non-ribosomal peptides. Antimicrobial compounds in the B. velezensis MV2 were extracted from culture supernatants using hydrophobic interaction chromatography. The crude extracts showed antimicrobial activity against both gram-positive bacteria and gram-negative bacteria; however, they were more effective against gram-positive bacteria. The extracts also showed antifungal activity against phytopathogenic fungi such as Fusarium fujikuroi and F. graminearum. In time-kill assays, these antimicrobial compounds showed bactericidal activity against Bacillus cereus, used as indicator strain. To predict the type of antimicrobial compounds produced by this strain, we used the antiSMASH algorithm. Forty-seven secondary metabolites were predicted to be synthesized in MV2, and among them, fourteen were identified with a similarity of 80% or more with those previously identified. Based on the antimicrobial properties, the antimicrobial compounds may be nonribosomal peptides or polyketides. These compounds possess the potential to be used as biopesticides in the food and agricultural industry as an alternative to antibiotics.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.4
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• pp.307-317
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• 2021

In recent years, global warming has led to frequent climate change-related problems, and elevated temperatures, among adverse climatic factors, represent a critical problem negatively affecting crop growth and yield. In this context, the present study examined the physiological traits of wheat plants grown under high temperatures. Specifically, the effects of elevated temperatures on seed development after heading were evaluated, and the vegetation indices of different organs were assessed using hyperspectral analysis. Among physiological traits, leaf greenness and OJIP parameters were higher in the high-temperature treatment than in the control treatment. Similarly, the leaf photosynthetic rate during seed development was higher in the high-temperature treatment than in the control treatment. Moreover, temperature by organ was higher in the high-temperature treatment than in the control treatment; consequently, the leaf transpiration rate and stomatal conductance were higher in the control treatment than in the high-temperature treatment. On all measuring dates, the weight of spikes and seeds corresponding to the sink organs was greater in the high-temperature treatment than in the control treatment. Additionally, the seed growth rate was higher in the high-temperature treatment than in the control treatment 14 days after heading, which may be attributed to the higher redistribution of photosynthates at the early stage of seed development in the former. In hyperspectral analysis, the vegetation indices related to leaf chlorophyll content and nitrogen state were higher in the high-temperature treatment than in the control treatment after heading. Our results suggest that elevated temperatures after the booting stage positively affect wheat growth and yield.